Purpose: To evaluate the performance of transfer learning with CNNs in predicting IDH1 genotype. Method and Materials: AlexNet, GoogLeNet, ResNet and VGGNet were pre-trained on the large scale natural image database (ImageNet) and fine-tuned with T1CE and FLAIR images. The outputs of training set were utilized to train LR and SVM models. Besides, fused images combining FLAIR and T1CE were used to fine-tune pre-trained ImageNet models. Results: Performances were improved by fine-tuning the four architectures with fused images. Conclusion: Transfer learning with various CNNs (especially VGGNet) is powerful in predicting IDH1 genotype in grade Ⅱ/Ⅲ gliomas.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords